Random Forests | A Quest After Perspectiveshttps://iphysresearch.github.io/blog/tag/random-forests/Random ForestsHugo Blox Builder (https://hugoblox.com)en-usMon, 01 Sep 2025 00:00:00 +0000https://iphysresearch.github.io/blog/media/sharing.pngRandom Forestshttps://iphysresearch.github.io/blog/tag/random-forests/Learning and interpreting gravitational-wave features from CNNs with a random forest approachhttps://iphysresearch.github.io/blog/mypublication/2025_cnn_random_forest/Mon, 01 Sep 2025 00:00:00 +0000https://iphysresearch.github.io/blog/mypublication/2025_cnn_random_forest/<p>The classifier combines CNN outputs with quantities related to feature-map variance, signal strength, overlap, and amplitude. Random-forest attribution is used to examine their contributions to detection decisions.</p> <h2 id="sources">Sources</h2> <ul> <li><a href="https://iopscience.iop.org/article/10.1088/2632-2153/adfc27" target="_blank" rel="noopener">Publisher record</a></li> <li><a href="https://arxiv.org/abs/2505.20357" target="_blank" rel="noopener">arXiv abstract and submission history</a></li> <li><a href="https://api.crossref.org/works/10.1088/2632-2153/adfc27" target="_blank" rel="noopener">Publisher metadata via Crossref</a></li> </ul>